FIG. 1 is a diagram showing an example of a distributed data management system. In the example shown, there are N user nodes (including user nodes 106 and 108) which are connected to a master node (100) via a network (104). Master node 100 includes a catalog (102) which contains metadata. Metadata is data about data and may (for example) describe the state of the system and/or the contents of user data in databases 110 and 112, such as properties, characteristics, attributes, features associated with the system (including user data or metadata in the system), or functions which are available and/or permitted to be performed on data objects and/or metadata objects. Copies of portions of catalog 102 are stored locally on the user nodes in a persistent manner in segments 114 and 116. For example, metadata may include processes or functions and if it is desired for that process to be performed at user node 106 on local data (e.g., user data 110), then segment 114 may include that function. In order to ensure that the system behaves properly, segments 114 and 116 must be synchronized with the catalog. Administratively, this and other management tasks may be difficult. For example, if a process in catalog 102 is updated, it must be ensured that the update is also propagated to segments 114 and 116 as needed. In some cases there is an automated process for doing this, but it is not a complete solution (e.g., it was assembled ad hoc and not all processes are copied by the code), it is not feasible to do a full replication, and/or the code is disorganized and thus it is difficult to update (e.g., if a new user node is added or a new process is added which needs to be synchronized across master node and user nodes). As such, an update may not be performed properly and may require manual intervention at a user node by an expert administrator. In the meantime, user data at the user node may be unavailable, which may disrupt work. New data management systems which are easier to manage would be desirable.